Genetics and Bioinformatics

Kristel Van Steen, PhD2

Montefiore Institute - Systems and Modeling

GIGA - Bioinformatics

ULg

kristel.vansteen@ulg.ac.be

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Lecture 1: Setting the pace

1 Bioinformatics – what’s in a name ?

Genetics

Molecular biology

Bioinformatics

2 Evolving trends in bioinformatics

Challenges

Topics in bioinformatics from a journal’s perspective

3 Bioinformatics software

R and Bioconductor

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1 Bioinformatics – what’s in a name?

Genetics

Genetics is the study of how traits

such as hair color, eye color, and

risk for disease are passed

(“inherited”) from parents to their

children. Genetics influence how

these inherited traits can be

different from person to person.

Your genetic information is called

your genetic code or genome.

Your genome is made up of a

chemical called deoxyribonucleic

acid (DNA) and is stored in almost

every cell in your body.

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Molecular biology

(adapted from: Davies et al 2009, Integrative genomics and functional explanation)

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Epigenetics – could the central dogma be in danger?

If parents are able to pass environmental information, in the form of

epigenetic modifications, on to their offspring as well as their genetic code,

epigenetic inheritance adds a whole new dimension to the modern picture of

evolution. For over a hundred years we have accepted that the genetic code

changes slowly, through the processes of random mutation and natural

selection. Epigenetics creates the possibility for a much more rapid response

to signals from the environment. It requires a completely different concept of

information transfer – experiences had generations ago, such as a famine

during your grandmothers time, could influence the way that your body

develops, even in today’s more plentiful western world.

(http://blogs.mcgill.ca/osscontributors/2014/01/07/)

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Computational biology

Biology = noun

Computational = adjective

When I use my method (or those of others) to answer a biological question, I

am doing science. I am learning new biology. The criteria for success has

little to do with the computational tools that I use, and is all about whether

the new biology is true and has been validated appropriately and to the

standards of evidence expected among the biological community. The papers

that result report new biological knowledge and are science papers. This is

computational biology. (https://rbaltman.wordpress.com)

Computational biology = the study of biology using computational techniques. The goal is to learn new biology, knowledge about living sytems. It is about science.

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Bioinformatics

Bio(logy) + Informatics (2 nouns)

When I build a method (usually as software, and with my staff, students, post-

docs–I never unfortunately do it myself anymore), I am engaging in an

engineering activity: I design it to have certain performance characteristics, I

build it using best engineering practices, I validate that it performs as I

intended, and I create it to solve not just a single problem, but a class of

similar problems that all should be solvable with the software. I then write

papers about the method, and these are engineering papers. This is

bioinformatics. (https://rbaltman.wordpress.com)

Bioinformatics = the creation of tools (algorithms, databases) that solve

problems. The goal is to build useful tools that work on biological data. It is

about engineering.

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Genetic epidemiology

Genetic epidemiology is a particular sub-discipline of epidemiology which

considers genetic influences on human traits.

As with any other epidemiologic studies, genetic epidemiology studies aim

- to assess the public health importance of diseases,

- to identify the populations at risk,

- to identify the causes of the disease,

- and to evaluate potential treatment or prevention strategies based on

those findings

Strategies of analysis include population studies and family studies.

- Huge challenge is to combine big data repositories from population or

family-studies (e.g., genomics, transcriptomics, metagenomics,

metabolomics, epigenomics, ….) with clinical and demographic data:

BIOINFORMATICS

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Integration with high-throughput omics (data-bases)

(Janet Thornton, EBI)

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2 Evolving trends in bioinformatics

Challenges in bioinformatics

Data deluge (availability, what to archive and what not?, …)

Knowledge management (accessibility, usability, …)

Predicting, not just explaining (what comes first: hypothesis generation,

data collection? …)

Precision medicine (alias: personalized medicine; holistic approach –

correlating different causal associations – versus a reductionist approach –

targeting very specific biomarkers, negative gold standards … negative

controls)

Speciation (loss in biodiversity, evolutionary units, “integrative taxonomy”:

molecular, morphological, ecological and environmental information)

Inferring the tree of life (unresolved orthology assignment, gene sampling

pyramid

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Topics in bioinformatics from a journal’s perspective

(source: Scope of the journal “Bioinformatics”)

Data and Text Mining This category includes: New methods and tools for extracting biological information from text, databases and other sources of information. Description of tools to organize, distribute and represent this information. New methods for inferring and predicting biological features based on the extracted

information. The submission of databases and repositories of annotated text, computational tools and general methodology for the work in this area are encouraged, provided that they have been previously tested.

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The journal ….

BioData Mining is an open access, peer reviewed, online journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data.

Topical areas include, but are not limited to:

Development, evaluation, and application of novel data mining and machine learning algorithms.

Adaptation, evaluation, and application of traditional data mining and machine learning algorithms.

Open-source software for the application of data mining and machine learning algorithms.

Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies.

Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.

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Databases and Ontologies

This category includes: Curated biological databases, data warehouses, eScience, web services, database integration, biologically-relevant ontologies.

(Kim et al. 2012)

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Bioimage Informatics This category includes novel methods for the acquisition, analysis and modeling of images produced by modern microscopy, with an emphasis on the application of innovative computational methods to solve challenging and significant biological problems at the molecular, sub-cellular, cellular, and tissue levels. This category also encourages large-scale image informatics methods/applications/software, joint analysis of multiple heterogeneous datasets that include images as a component, and development of bioimage-related ontologies and image retrieval methods.

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Genetics and Population Analysis This category includes: Segregation analysis, linkage analysis, association analysis, map construction, population simulation, haplotyping, linkage disequilibrium, pedigree drawing, marker discovery, power calculation, genotype calling.

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Genome analysis This category includes: Comparative genomics, genome assembly, genome and chromosome annotation, identification of genomic features such as genes, splice sites and promoters.

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Sequence analysis This category includes: Multiple sequence alignment, sequence searches and clustering; prediction of function and localisation; novel domains and motifs; prediction of protein, RNA and DNA functional sites and other sequence features.

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Phylogenetics

This category includes: novel phylogeny estimation procedures for molecular data including nucleotide sequence data, amino acid data, whole genomes, SNPs, etc., simultaneous multiple sequence alignment and phylogeny estimation, phylogenetic approaches for any aspect of molecular sequence analysis (see Sequence Analysis scope), models of molecular evolution, assessments of statistical support of resulting phylogenetic estimates, comparative methods, coalescent theory, approaches for comparing phylogenetic trees, methods for testing and/or mapping character change along a phylogeny.

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Gene Expression This category includes a wide range of applications relevant to the high-throughput analysis of expression of biological quantities, including microarrays (nucleic acid, protein, array CGH, genome tiling, and other arrays), RNA-seq, proteomics and mass spectrometry. Approaches to data analysis to be considered include statistical analysis of differential gene expression; expression-based classifiers; methods to determine or describe regulatory networks; pathway analysis; …

(Moore 2005)

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Systems Biology This category includes whole cell approaches to molecular biology. Any combination of experimentally collected whole cell systems, pathways or signaling cascades on RNA, proteins, genomes or metabolites that advances the understanding of molecular biology or molecular medicine will be considered. Interactions and binding within or between any of the categories will be considered including protein interaction networks, regulatory networks, metabolic and signaling pathways. Detailed analysis of the biological properties of the systems are of particular interest.

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A sample of literature-based bioinformatics resources

BioData Mining

Bioinformatics

BMC Bioinformatics

Briefings in Bioinformatics

Genome Biology

Genome Medicine

Journal of Integrative Bioinformatics

(http://www.bioinformatics.org/wiki/Journals)

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3 Bioinformatics software

Data access and analysis – data mining

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Principal internet resources for genome browsers and databases

Resource Web address Description Sponsoring

organizations

Open Helix http://www.openhelix.com/tutorials.shtml

On-line tutorial material for all

of the genome databases. OpenHelix, LLC

UCSC

Genome

Browser

http://genome.ucsc.edu

Comprehensive, multi-species

genome database providing

genome browsing and batch

querying.

Genome Bioinformatics

Group, University of

California, Santa Cruz

Ensembl

Browser http://www.ensembl.org

Comprehensive, multi-species

genome database providing

genome browsing and batch

querying.

European

Bioinformatics Institute

(EBI) and the Sanger

Center

NCBI

MapViewer http://www.ncbi.nlm.nih.gov/mapview

Multi-species genome browser

focusing especially on genome

mapping applications.

National Center for

Biotechnology

Information (NCBI)

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Biomart http://www.biomart.org/

Genome-database, batch-querying

interface used by Ensembl and

several single-genome databases.

Ontario Institute for Cancer

Research and European

Bioinformatics Institute

Galaxy http://main.g2.bx.psu.edu

Integrated toolset for analyzing

genome batch-querying data.

Center for Comparative Genomics

and Bioinformatics. Penn State

University

Taverna http://taverna.sourceforge.net

Toolset for creating pipelines of

bioinformatics analyses implemented

via the Web services protocol.

Open Middleware Infrastructure

Institute, University of

Southampton (OMII-UK)

GMOD http://www.gmod.org

Repository of software tools for

developing generic genome

databases.

A consortium of organizations

operating as the Generic Model

Organism Database project

(Schattner et al. 2009)

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Bioinformatics tools

(http://www.ccmb.med.umich.edu/bioinf-core/tools)

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Bioconductor (TA- sessions)

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(http://www.bioconductor.org/)

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R (TA- sessions)

R is a freely available language and environment for statistical computing

and graphics which provides a wide variety of statistical and graphical

techniques: linear and nonlinear modelling, statistical tests, time series

analysis, classification, clustering, etc.

Consult the R project homepage for further information.

The “R-community” is very responsive in addressing practical questions

with the software (but consult the FAQ pages first!)

CRAN is a network of ftp and web servers around the world that store

identical, up-to-date, versions of code and documentation for R.

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(https://www.r-project.org/)

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The R graph gallery

One of R’s strengths is the ease with which well-designed publication-

quality plots can be produced …